DATA: Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds

Jaewook Lee; Haneul Ko; Joonwoo Kim; Sangheon Pack

doi:10.1109/TII.2020.2990674

DATA: Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds

Jaewook Lee, Haneul Ko, Joonwoo Kim, Sangheon Pack

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation (DATA) algorithm. Evaluation results demonstrate that DATA can reduce the application completion time up to by 15%-32% compared to conventional dependency-unaware task allocation schemes.

Original language	English
Article number	9079591
Pages (from-to)	7782-7790
Number of pages	9
Journal	IEEE Transactions on Industrial Informatics
Volume	16
Issue number	12
DOIs	https://doi.org/10.1109/TII.2020.2990674
Publication status	Published - 2020 Dec

Keywords

Distributed edge cloud
heuristic algorithm
mixed integer nonlinear program (MINLP)
optimization

ASJC Scopus subject areas

Control and Systems Engineering
Information Systems
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TII.2020.2990674

Fingerprint Dive into the research topics of 'DATA: Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds'. Together they form a unique fingerprint.

Cite this

@article{0296e9c7331a4ee79cba625a04143a57,

title = "DATA: Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds",

abstract = "To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation (DATA) algorithm. Evaluation results demonstrate that DATA can reduce the application completion time up to by 15%-32% compared to conventional dependency-unaware task allocation schemes.",

keywords = "Distributed edge cloud, heuristic algorithm, mixed integer nonlinear program (MINLP), optimization",

author = "Jaewook Lee and Haneul Ko and Joonwoo Kim and Sangheon Pack",

note = "Funding Information: This work was supported in part by the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) under Grant 2020R1A2C3006786 and in part by the Samsung Research in Samsung Electronics. Paper no. TII-19-4415. Funding Information: Manuscript received September 28, 2019; revised January 5, 2020; accepted April 15, 2020. Date of publication April 27, 2020; date of current version September 18, 2020. This work was supported in part by the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) under Grant 2020R1A2C3006786 and in part by the Samsung Research in Samsung Electronics. Paper no. TII-19-4415. (Corresponding author: Sangheon Pack.) Jaewook Lee, Joonwoo Kim, and Sangheon Pack are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: iioiioiio123@korea.ac.kr; starjoon0202@korea.ac.kr; shpack@korea.ac.kr). Publisher Copyright: {\textcopyright} 2005-2012 IEEE.",

year = "2020",

month = dec,

doi = "10.1109/TII.2020.2990674",

language = "English",

volume = "16",

pages = "7782--7790",

journal = "IEEE Transactions on Industrial Informatics",

issn = "1551-3203",

publisher = "IEEE Computer Society",

number = "12",

}

TY - JOUR

T1 - DATA

T2 - Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds

AU - Lee, Jaewook

AU - Ko, Haneul

AU - Kim, Joonwoo

AU - Pack, Sangheon

N1 - Funding Information: This work was supported in part by the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) under Grant 2020R1A2C3006786 and in part by the Samsung Research in Samsung Electronics. Paper no. TII-19-4415. Funding Information: Manuscript received September 28, 2019; revised January 5, 2020; accepted April 15, 2020. Date of publication April 27, 2020; date of current version September 18, 2020. This work was supported in part by the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) under Grant 2020R1A2C3006786 and in part by the Samsung Research in Samsung Electronics. Paper no. TII-19-4415. (Corresponding author: Sangheon Pack.) Jaewook Lee, Joonwoo Kim, and Sangheon Pack are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: iioiioiio123@korea.ac.kr; starjoon0202@korea.ac.kr; shpack@korea.ac.kr). Publisher Copyright: © 2005-2012 IEEE.

PY - 2020/12

Y1 - 2020/12

N2 - To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation (DATA) algorithm. Evaluation results demonstrate that DATA can reduce the application completion time up to by 15%-32% compared to conventional dependency-unaware task allocation schemes.

AB - To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation (DATA) algorithm. Evaluation results demonstrate that DATA can reduce the application completion time up to by 15%-32% compared to conventional dependency-unaware task allocation schemes.

KW - Distributed edge cloud

KW - heuristic algorithm

KW - mixed integer nonlinear program (MINLP)

KW - optimization

UR - http://www.scopus.com/inward/record.url?scp=85092106536&partnerID=8YFLogxK

U2 - 10.1109/TII.2020.2990674

DO - 10.1109/TII.2020.2990674

M3 - Article

AN - SCOPUS:85092106536

VL - 16

SP - 7782

EP - 7790

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 12

M1 - 9079591

ER -

DATA: Dependency-Aware Task Allocation Scheme in Distributed Edge Clouds

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this